Overview

Dataset statistics

Number of variables35
Number of observations77
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.2 KiB
Average record size in memory281.7 B

Variable types

CAT19
NUM13
BOOL3

Warnings

TempDist has constant value "77" Constant
temporalGlobalLoc has constant value "77" Constant
spatialGlobalLoc has constant value "77" Constant
temporalInternalLoc has constant value "77" Constant
spatialInternalLoc has constant value "77" Constant
AUrs2 has constant value "77" Constant
Alkoh has constant value "77" Constant
Bes2 has constant value "77" Constant
Zust2 has constant value "77" Constant
StrklVu has constant value "77" Constant
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
Month is highly correlated with df_indexHigh correlation
df_index is highly correlated with MonthHigh correlation
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
df_index has unique values Unique
Strasse has 17 (22.1%) zeros Zeros
UArt1 has 2 (2.6%) zeros Zeros
Fstf has 5 (6.5%) zeros Zeros
Month has 3 (3.9%) zeros Zeros

Reproduction

Analysis started2020-10-21 16:54:56.174892
Analysis finished2020-10-21 16:56:10.107515
Duration1 minute and 13.93 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct77
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean940.3376623
Minimum9
Maximum1826
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:56:10.438871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile152
Q1545
median955
Q31354
95-th percentile1683.6
Maximum1826
Range1817
Interquartile range (IQR)809

Descriptive statistics

Standard deviation508.087529
Coefficient of variation (CV)0.540324555
Kurtosis-1.130909118
Mean940.3376623
Median Absolute Deviation (MAD)410
Skewness-0.046207369
Sum72406
Variance258152.9371
MonotocityStrictly increasing
2020-10-21T18:56:15.205738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
124811.3%
 
55911.3%
 
131911.3%
 
157711.3%
 
55411.3%
 
68311.3%
 
108511.3%
 
163211.3%
 
30211.3%
 
30411.3%
 
Other values (67)6787.0%
 
ValueCountFrequency (%) 
911.3%
 
5711.3%
 
7711.3%
 
14811.3%
 
15311.3%
 
ValueCountFrequency (%) 
182611.3%
 
180211.3%
 
176111.3%
 
169411.3%
 
168111.3%
 

TempExMax
Real number (ℝ≥0)

Distinct60
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.2597403
Minimum12
Maximum1323
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:56:22.874028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile30
Q175
median144
Q3234
95-th percentile601.2
Maximum1323
Range1311
Interquartile range (IQR)159

Descriptive statistics

Standard deviation227.1430342
Coefficient of variation (CV)1.151492108
Kurtosis13.42688182
Mean197.2597403
Median Absolute Deviation (MAD)72
Skewness3.387845686
Sum15189
Variance51593.95796
MonotocityNot monotonic
2020-10-21T18:56:23.020182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16233.9%
 
15033.9%
 
12922.6%
 
9922.6%
 
4222.6%
 
6922.6%
 
7222.6%
 
7522.6%
 
3622.6%
 
3022.6%
 
Other values (50)5571.4%
 
ValueCountFrequency (%) 
1211.3%
 
1511.3%
 
1811.3%
 
3022.6%
 
3622.6%
 
ValueCountFrequency (%) 
132311.3%
 
125711.3%
 
81311.3%
 
65411.3%
 
58811.3%
 

SpatExMax
Real number (ℝ≥0)

Distinct76
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9714.194805
Minimum1194
Maximum37965
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:56:23.171936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1194
5-th percentile2004
Q13957
median7621
Q312907
95-th percentile28523
Maximum37965
Range36771
Interquartile range (IQR)8950

Descriptive statistics

Standard deviation7763.878204
Coefficient of variation (CV)0.7992302357
Kurtosis2.455980489
Mean9714.194805
Median Absolute Deviation (MAD)4032
Skewness1.557680426
Sum747993
Variance60277804.76
MonotocityNot monotonic
2020-10-21T18:56:23.310608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1102322.6%
 
412411.3%
 
2218711.3%
 
604811.3%
 
324411.3%
 
912311.3%
 
1706911.3%
 
987511.3%
 
119411.3%
 
785211.3%
 
Other values (66)6685.7%
 
ValueCountFrequency (%) 
119411.3%
 
128011.3%
 
166911.3%
 
194811.3%
 
201811.3%
 
ValueCountFrequency (%) 
3796511.3%
 
3233911.3%
 
2879911.3%
 
2854711.3%
 
2851711.3%
 

TempDist
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
0
77 
ValueCountFrequency (%) 
077100.0%
 
2020-10-21T18:56:23.416808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

SpatDist
Real number (ℝ≥0)

Distinct67
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372.8961039
Minimum18
Maximum897
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:56:26.194432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile50
Q1158
median290
Q3610
95-th percentile768.4
Maximum897
Range879
Interquartile range (IQR)452

Descriptive statistics

Standard deviation255.5725041
Coefficient of variation (CV)0.6853718809
Kurtosis-1.136785453
Mean372.8961039
Median Absolute Deviation (MAD)178
Skewness0.4400535622
Sum28713
Variance65317.30485
MonotocityNot monotonic
2020-10-21T18:56:26.336629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
25056.5%
 
75033.9%
 
25222.6%
 
11222.6%
 
71822.6%
 
5022.6%
 
63811.3%
 
58311.3%
 
15811.3%
 
16111.3%
 
Other values (57)5774.0%
 
ValueCountFrequency (%) 
1811.3%
 
2111.3%
 
2211.3%
 
5022.6%
 
6011.3%
 
ValueCountFrequency (%) 
89711.3%
 
88011.3%
 
87011.3%
 
77011.3%
 
76811.3%
 

Coverage
Real number (ℝ≥0)

Distinct51
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.03896104
Minimum8
Maximum100
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:56:27.068301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14.6
Q125
median39
Q355
95-th percentile84.4
Maximum100
Range92
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.70886404
Coefficient of variation (CV)0.5044002809
Kurtosis-0.06765055137
Mean43.03896104
Median Absolute Deviation (MAD)15
Skewness0.6461080903
Sum3314
Variance471.2747779
MonotocityNot monotonic
2020-10-21T18:56:28.946750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4845.2%
 
3633.9%
 
5133.9%
 
2133.9%
 
3922.6%
 
3022.6%
 
3322.6%
 
3522.6%
 
3822.6%
 
4022.6%
 
Other values (41)5267.5%
 
ValueCountFrequency (%) 
811.3%
 
911.3%
 
1011.3%
 
1311.3%
 
1511.3%
 
ValueCountFrequency (%) 
10011.3%
 
9611.3%
 
9011.3%
 
8611.3%
 
8422.6%
 

temporalGlobalLoc
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
3
77 
ValueCountFrequency (%) 
377100.0%
 
2020-10-21T18:56:32.507846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:56:38.015248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:40.490100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

spatialGlobalLoc
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
0
77 
ValueCountFrequency (%) 
077100.0%
 
2020-10-21T18:56:43.390528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

temporalInternalLoc
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
77 
ValueCountFrequency (%) 
-177100.0%
 
2020-10-21T18:56:43.449258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:56:43.516619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:46.241846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

spatialInternalLoc
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
77 
ValueCountFrequency (%) 
-177100.0%
 
2020-10-21T18:56:49.054841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:56:49.123753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:49.196535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

TimeLossCar
Real number (ℝ≥0)

Distinct76
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1526.064935
Minimum1005
Maximum1997
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:56:49.752598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1005
5-th percentile1051.4
Q11275
median1531
Q31758
95-th percentile1944.6
Maximum1997
Range992
Interquartile range (IQR)483

Descriptive statistics

Standard deviation287.9646971
Coefficient of variation (CV)0.1886975387
Kurtosis-1.097053336
Mean1526.064935
Median Absolute Deviation (MAD)238
Skewness-0.1992071461
Sum117507
Variance82923.66678
MonotocityNot monotonic
2020-10-21T18:56:52.510322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
175822.6%
 
196611.3%
 
157211.3%
 
151611.3%
 
133711.3%
 
183311.3%
 
106711.3%
 
154611.3%
 
161811.3%
 
178911.3%
 
Other values (66)6685.7%
 
ValueCountFrequency (%) 
100511.3%
 
101211.3%
 
101311.3%
 
102511.3%
 
105811.3%
 
ValueCountFrequency (%) 
199711.3%
 
198111.3%
 
196611.3%
 
194711.3%
 
194411.3%
 

TimeLossHGV
Real number (ℝ≥0)

Distinct72
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean748.2337662
Minimum502
Maximum992
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:56:52.657048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum502
5-th percentile518.4
Q1624
median732
Q3879
95-th percentile980.2
Maximum992
Range490
Interquartile range (IQR)255

Descriptive statistics

Standard deviation149.0740628
Coefficient of variation (CV)0.1992346103
Kurtosis-1.217659977
Mean748.2337662
Median Absolute Deviation (MAD)134
Skewness0.0671225026
Sum57614
Variance22223.07621
MonotocityNot monotonic
2020-10-21T18:56:54.229507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
57722.6%
 
63322.6%
 
99222.6%
 
72922.6%
 
69722.6%
 
56811.3%
 
82311.3%
 
69411.3%
 
56511.3%
 
92711.3%
 
Other values (62)6280.5%
 
ValueCountFrequency (%) 
50211.3%
 
50911.3%
 
51011.3%
 
51611.3%
 
51911.3%
 
ValueCountFrequency (%) 
99222.6%
 
99011.3%
 
98911.3%
 
97811.3%
 
97011.3%
 

Strasse
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.636363636
Minimum0
Maximum9
Zeros17
Zeros (%)22.1%
Memory size616.0 B
2020-10-21T18:56:56.376001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile7.2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.282133644
Coefficient of variation (CV)0.8656368996
Kurtosis0.7206721462
Mean2.636363636
Median Absolute Deviation (MAD)1
Skewness1.035979807
Sum203
Variance5.208133971
MonotocityNot monotonic
2020-10-21T18:56:58.065365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
22937.7%
 
01722.1%
 
31013.0%
 
567.8%
 
445.2%
 
733.9%
 
922.6%
 
822.6%
 
622.6%
 
122.6%
 
ValueCountFrequency (%) 
01722.1%
 
122.6%
 
22937.7%
 
31013.0%
 
445.2%
 
ValueCountFrequency (%) 
922.6%
 
822.6%
 
733.9%
 
622.6%
 
567.8%
 

Kat
Categorical

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size616.0 B
7
45 
3
28 
2
 
4
ValueCountFrequency (%) 
74558.4%
 
32836.4%
 
245.2%
 
2020-10-21T18:56:59.371951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:56:59.440382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:06.663034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Categorical

Distinct4
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size616.0 B
6
54 
1
13 
7
 
5
3
 
5
ValueCountFrequency (%) 
65470.1%
 
11316.9%
 
756.5%
 
356.5%
 
2020-10-21T18:57:06.777510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:06.858818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:14.428080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Betei
Real number (ℝ≥0)

Distinct5
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.168831169
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:57:14.526558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3.2
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8944654005
Coefficient of variation (CV)0.4124181787
Kurtosis10.39394032
Mean2.168831169
Median Absolute Deviation (MAD)0
Skewness2.262028557
Sum167
Variance0.8000683527
MonotocityNot monotonic
2020-10-21T18:57:14.754832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
24761.0%
 
31418.2%
 
11215.6%
 
433.9%
 
711.3%
 
ValueCountFrequency (%) 
11215.6%
 
24761.0%
 
31418.2%
 
433.9%
 
711.3%
 
ValueCountFrequency (%) 
711.3%
 
433.9%
 
31418.2%
 
24761.0%
 
11215.6%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.584415584
Minimum0
Maximum9
Zeros2
Zeros (%)2.6%
Memory size616.0 B
2020-10-21T18:57:14.862758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.467422741
Coefficient of variation (CV)0.688375185
Kurtosis-0.1164947079
Mean3.584415584
Median Absolute Deviation (MAD)1
Skewness1.126233946
Sum276
Variance6.088174983
MonotocityNot monotonic
2020-10-21T18:57:14.948803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
23140.3%
 
32127.3%
 
81215.6%
 
556.5%
 
933.9%
 
133.9%
 
022.6%
 
ValueCountFrequency (%) 
022.6%
 
133.9%
 
23140.3%
 
32127.3%
 
556.5%
 
ValueCountFrequency (%) 
933.9%
 
81215.6%
 
556.5%
 
32127.3%
 
23140.3%
 

UArt2
Categorical

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
69 
3
 
5
9
 
3
ValueCountFrequency (%) 
-16989.6%
 
356.5%
 
933.9%
 
2020-10-21T18:57:18.815578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:24.516137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:29.136834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.896103896
Min length1

AUrs1
Categorical

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size616.0 B
0
71 
73
 
6
ValueCountFrequency (%) 
07192.2%
 
7367.8%
 
2020-10-21T18:57:29.256583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:29.338729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:33.090904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.077922078
Min length1

AUrs2
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
0
77 
ValueCountFrequency (%) 
077100.0%
 
2020-10-21T18:57:33.169148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

AufHi
Categorical

Distinct4
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
59 
3
12 
4
 
4
5
 
2
ValueCountFrequency (%) 
-15976.6%
 
31215.6%
 
445.2%
 
522.6%
 
2020-10-21T18:57:33.244391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:33.325281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:40.971982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.766233766
Min length1

Alkoh
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
77 
ValueCountFrequency (%) 
-177100.0%
 
2020-10-21T18:57:41.077980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:41.148214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:41.216107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Char1
Categorical

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
72 
4
 
3
5
 
2
ValueCountFrequency (%) 
-17293.5%
 
433.9%
 
522.6%
 
2020-10-21T18:57:41.334680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:41.421527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:43.352991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.935064935
Min length1

Char2
Categorical

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
75 
6
 
2
ValueCountFrequency (%) 
-17597.4%
 
622.6%
 
2020-10-21T18:57:43.474015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:43.556792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:45.570062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.974025974
Min length1

Bes1
Categorical

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
57 
6
20 
ValueCountFrequency (%) 
-15774.0%
 
62026.0%
 
2020-10-21T18:57:46.853299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:48.785707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:52.605851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.74025974
Min length1

Bes2
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
77 
ValueCountFrequency (%) 
-177100.0%
 
2020-10-21T18:57:52.713266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:52.784069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:52.850155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Lich1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size616.0 B
0
62 
2
14 
-1
 
1
ValueCountFrequency (%) 
06280.5%
 
21418.2%
 
-111.3%
 
2020-10-21T18:57:52.987375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)1.3%
2020-10-21T18:57:53.070620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:57:58.964954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.012987013
Min length1

Lich2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
63 
4
14 
ValueCountFrequency (%) 
-16381.8%
 
41418.2%
 
2020-10-21T18:57:59.086462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:57:59.296282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:58:01.345995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.818181818
Min length1

Zust1
Categorical

Distinct4
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size616.0 B
0
61 
1
14 
-1
 
1
2
 
1
ValueCountFrequency (%) 
06179.2%
 
11418.2%
 
-111.3%
 
211.3%
 
2020-10-21T18:58:01.466011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)2.6%
2020-10-21T18:58:01.558625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:58:03.609607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.012987013
Min length1

Zust2
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
77 
ValueCountFrequency (%) 
-177100.0%
 
2020-10-21T18:58:03.727179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:58:03.812830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:58:03.880753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Fstf
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.103896104
Minimum0
Maximum5
Zeros5
Zeros (%)6.5%
Memory size616.0 B
2020-10-21T18:58:03.975159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.438058043
Coefficient of variation (CV)0.6835214157
Kurtosis-0.1401869823
Mean2.103896104
Median Absolute Deviation (MAD)1
Skewness0.8763493853
Sum162
Variance2.068010936
MonotocityNot monotonic
2020-10-21T18:58:05.275218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
22633.8%
 
12633.8%
 
51013.0%
 
367.8%
 
056.5%
 
445.2%
 
ValueCountFrequency (%) 
056.5%
 
12633.8%
 
22633.8%
 
367.8%
 
445.2%
 
ValueCountFrequency (%) 
51013.0%
 
445.2%
 
367.8%
 
22633.8%
 
12633.8%
 

StrklVu
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size616.0 B
A
77 
ValueCountFrequency (%) 
A77100.0%
 
2020-10-21T18:58:05.383214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:58:05.457805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:58:06.434896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

WoTag
Real number (ℝ≥0)

Distinct7
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.025974026
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size616.0 B
2020-10-21T18:58:06.534422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.708908747
Coefficient of variation (CV)0.4244708822
Kurtosis-1.029456295
Mean4.025974026
Median Absolute Deviation (MAD)1
Skewness-0.008921683454
Sum310
Variance2.920369105
MonotocityNot monotonic
2020-10-21T18:58:07.384414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
31519.5%
 
61418.2%
 
51316.9%
 
41316.9%
 
21215.6%
 
756.5%
 
156.5%
 
ValueCountFrequency (%) 
156.5%
 
21215.6%
 
31519.5%
 
41316.9%
 
51316.9%
 
ValueCountFrequency (%) 
756.5%
 
61418.2%
 
51316.9%
 
41316.9%
 
31519.5%
 

FeiTag
Categorical

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size616.0 B
-1
73 
1
 
4
ValueCountFrequency (%) 
-17394.8%
 
145.2%
 
2020-10-21T18:58:08.705950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T18:58:08.782832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:58:10.718843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.948051948
Min length1

Month
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.896103896
Minimum0
Maximum11
Zeros3
Zeros (%)3.9%
Memory size616.0 B
2020-10-21T18:58:12.982407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q38
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.971728257
Coefficient of variation (CV)0.5040155854
Kurtosis-0.8539557511
Mean5.896103896
Median Absolute Deviation (MAD)2
Skewness-0.2392093973
Sum454
Variance8.831168831
MonotocityIncreasing
2020-10-21T18:58:15.351167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
61013.0%
 
8911.7%
 
7911.7%
 
9810.4%
 
579.1%
 
479.1%
 
1067.8%
 
267.8%
 
356.5%
 
145.2%
 
Other values (2)67.8%
 
ValueCountFrequency (%) 
033.9%
 
145.2%
 
267.8%
 
356.5%
 
479.1%
 
ValueCountFrequency (%) 
1133.9%
 
1067.8%
 
9810.4%
 
8911.7%
 
7911.7%
 

Interactions

2020-10-21T18:55:02.829798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:03.212578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:03.572271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:03.942690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:04.314686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:04.682593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:05.046555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:05.445632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:05.813195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:06.183607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:06.565109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:06.950460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:07.321217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:14.661734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:14.684052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:14.979057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:20.859464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:22.048090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:27.571199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:32.819506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:35.719430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:40.366626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:45.154809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:46.151778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:47.126622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:48.103602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:50.456790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:50.482140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:50.604797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:50.714369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:50.954356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:51.059637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:51.156640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:51.266395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:51.367673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:51.467438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:51.565419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:51.661600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:51.764089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.233082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.251255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.340926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.435486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.519632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.600199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.687153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.776993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.861289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:52.947733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:53.033136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:53.118501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:53.209080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:53.678129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:53.697901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:53.789201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:53.882441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:53.967314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:54.052330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:54.140204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:54.230736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:54.315344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:54.403780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:54.487869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:54.722027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:54.820603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.288574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.307507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.407151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.508047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.599545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.687189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.778739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.868772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:55.956247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:56.051634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:56.135329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:56.229733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:56.321731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:56.797960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:56.816321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:56.911621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.018651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.107807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.201898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.301936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.408800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.502381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.594421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.680207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.776286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:57.882956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:58.370758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:58.389185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:58.480532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:58.579453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:58.661903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:58.893130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:58.990802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:59.079847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:59.159608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:59.253040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:59.349383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:59.477160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:55:59.586250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:00.274608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:00.301808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:00.580405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:00.754922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:00.858572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:00.980599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:01.091082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:01.199699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:01.298943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:01.409370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:01.506902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:01.613140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:01.716039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:02.233764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:02.253501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:02.365768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:02.481594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:02.631066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:02.757983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:02.860840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:02.956854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:03.038569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:03.154881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:03.238214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:03.323876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:03.564881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.054512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.077457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.188764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.287614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.374797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.465003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.556873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.666592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.755321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.847398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:04.935216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:05.028071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:05.132788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:05.612427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:05.634173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:05.736311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:05.835879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:05.929359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:06.022647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:06.129235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:06.231452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:06.323128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:06.421605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:06.513027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:06.610600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:06.719060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:07.197846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:07.218237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:07.313017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:07.407480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:07.493019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:07.725800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:07.828366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:07.920810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:08.009270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:08.106109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:08.199398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:08.288689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:08.381995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-21T18:58:15.884006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-21T18:58:16.355263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-21T18:58:16.838106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-21T18:58:17.287310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-21T18:58:17.339713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-21T18:56:09.344544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T18:56:10.066511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempExMaxSpatExMaxTempDistSpatDistCoveragetemporalGlobalLocspatialGlobalLoctemporalInternalLocspatialInternalLocTimeLossCarTimeLossHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonth
09105994101122630-1-1155764103632-100-1-1-1-1-1-1241-10A6-10
15739194807112330-1-1123566717720-100-1-1-1-1-1-10-12-10A4-10
27775262907268430-1-1164862427623-100-1-1-1-1-1-1240-11A5-10
31482191658201842430-1-11012989073293003-1-1-1-1-10-10-11A7-11
415318128001615230-1-1199799037622-100-1-1-1-1-1-10-10-11A4-11
518872235102504630-1-1112058837622-100-1-1-1-1-1-10-10-12A3-11
61931501102305024830-1-1175889743632-100-1-1-1-1-1-10-10-12A6-11
723236347506104830-1-1180797807623-100-1-14-16-10-10-13A3-12
82423481102302282430-1-1177551607118-1003-156-1-10-11-14A6-12
93029927460965430-1-1130278833642-100-1-1-1-1-1-10-10-12A3-12

Last rows

df_indexTempExMaxSpatExMaxTempDistSpatDistCoveragetemporalGlobalLocspatialGlobalLoctemporalInternalLocspatialInternalLocTimeLossCarTimeLossHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonth
6715779034450503030-1-1178990107623-100-1-1-1-1-1-1240-15A5-19
681632126762101433630-1-1173450223642-100-1-1-1-1-1-10-10-15A4-110
6916502431393003412730-1-1171793457128-1004-1-1-16-1240-12A2-110
7016591323644602527230-1-1184196727623-100-1-1-1-16-1240-11A5-110
7116601291348401933030-1-1146297023632-100-1-1-1-1-1-1240-11A5-110
7216811441502902506330-1-1108151903672300-1-1-1-1-1-1241-10A2-110
731694234474704956130-1-1171186607623-100-1-1-1-1-1-1241-11A3-110
74176163463502902330-1-1171070327623-100-1-1-1-16-10-11-12A2-111
751802141374405486630-1-1143753967622-100-1-1-1-1-1-1240-11A2-111
761826871706903102130-1-1127587923622-100-1-1-1-1-1-10-10-11A7-111